Top 10 Machine Learning Books

In this article, we will be discussing the best books that are available on Machine Learning/ Artificial Intelligence. We know how much does it mean to when it comes to reading so we have handpicked some of the best selling and also popular books in this genre. One of the main vital qualities of being a Machine Learning guy is to be up to date with the latest market trends and also read a lot of books.

So here is our collection that lists out some of the best books available in Machine Learning/ Artificial Intelligence space, so let’s go into the topic straight away.

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10 Best Machine Learning Books

#1: Introduction to Artificial Intelligence

Written by Philip C Jackson

This book has been written almost 40 years ago and it was released as a second edition in the year 1985. Within this edition, it gives out the introduction on the science of reasoning processes in computers, different available approaches and results are posted from almost 20 years of research.

Some of the major concepts that are included in this book are:

1. Machine architecture
2. Automatic programming
3. Software techniques
4. Industrial automation
5. Predicate calculus theorem

This book would be ideal for any individual who is new to Artificial intelligence space where they would like to have a deeper understanding of the concepts.

#2: Machine Learning Yearning

Written by Andrew Ng

This book is all about Machine learning concepts and helps the individuals to understand the topic in-depth with real-time examples. Using machine learning capabilities system can take practical and appropriate decisions:

1. Whether to collect more training data or not
2. Can deep learning be used end to end
3. How to deal with training set to your test set etc

This book will be perfect for individuals who are looking to build their expertise in Machine learning and needs to understand the basics of it.

Related Article: What is the difference between Artificial Intelligence, Machine Learning

#Book 3: The Elements of Statistical Learning Data Mining, Inference and Prediction, Second Edition

Written by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

This book is essential and vital for individuals who are looking to build a good understanding of the following concepts:

1. Data Mining
2. Machine Learning
3. Bioinformatics
4. Neural network

All the topics are well defined and have provided in-depth information about these topics and have provided with necessary examples where ever it is necessary.

This book will be beneficial for individuals who are looking for advanced knowledge of Machine Learning. This is more relevant for techie individuals who are looking to brush up their skills quickly.

#Book4: Reinforcement Learning: An Introduction

Written by Richard. S. Sutton and Andrew G. Barto

The topic “Reinforcement learning” has got a lot of attention and has become one of the top picks in Artificial Intelligence Research. Further covering all the introduction to many sub topics within the book followed by algorithm explanation.

The following topics are covered within this book:

1. In-depth understanding of Deep Learning.
2. Dynamic programming, Monte Carlo methods, temporal difference learning concepts
3. Artificial neural networks and eligibility traces etc.

All of these topics are detailed in-depth and has provided a practical way of narration so that it will be easy for the users to read through and digest.

This book is ideal for individuals who are new to the field of Artificial Intelligence. It will provide in-depth knowledge about Reinforcement Learning concepts

Related Article: [Top 10 Machine Learning Algorithms]

 

#Book5: How to Create a Mind: The Secret of Human Thought Revealed

Written by Ray Kurzweil

This book talks about how future civilizations will be dominated by computers and automated machines. The written has mentioned how reverse engineering on the human mind has helped the entire technology advancement in this domain (i.e. Machine Learning advancements).

The following topics are covered in detail within the book:

1. Learning from the example
2. Quantification of uncertainty
3. Clear explanation of themes using agents
4. Communication and perception
5. Natural Language Processing

This book is important for individuals who are interested to know what the future has to offer to mankind and what are the advancements in Machine Learning. This is more a philosophy oriented book where the individuals can understand what can be expected in the future.

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Book6: Artificial Intelligence and Soft computing Behavioral and Cognitive Modeling of the Human Brain

Written by Amit Konar

This book is very famous and considered to be a standard when it comes to Artificial Intelligence. This books gives a platform for the individual to understand and then also apply the modern techniques and traditional techniques for Artificial Intelligence concepts.

1. Human Cognition
2. Non-monotonic
3. Spatio-temporal reasoning

The above topics are clearly and practically explained by the author.

This book is a perfect fit for those who are looking for a broader knowledge on Artificial Intelligence. It is one of the best resources for anyone who is involved in computer science.

Related Article: Machine Learning Applications

Book7: The Elements of Statistical Learning

Written by Trevor Hastie, Robert Tibshirani, Jerome Friedman

This book talks about the common conceptual framework. The entire book is narrated in such a way that it is more focused on the concepts rather than the use of mathematics. Anyone who is interested in data mining, this book will be a perfect choice.  

The following topics are discussed in this book:

1. Neural networks
2. Support vector machines
3. Classification trees

Book8: Understanding Machine Learning: From Theory to Algorithms

Written by Shai Shalev-Shwartz and Shai Ben-David

The sole purpose of writing this book is to bring awareness to the following topics:

1. Introduction to Machine Learning
2. Algorithms walkthrough
3. Mathematical derivations that are used to develop an algorithm
4. Concepts of Convexity
5. Neural networks
6. PAC Bayes approach

This book is ideal for individuals who are looking to build in-depth knowledge about Machine learning and good understanding of how the algorithms are built.

Related Article: Machine Learning Examples In Real World

Book 9: Python Machine Learning

Written by Sebastian Raschka

As the book title itself states the topics that are covered in the book are more catered towards Machine Learning by using Python. This book is more practical and offers in-depth information about Machine Learning. The concepts like “Predictive Analysis” is clearly explained in this book.

The book covers important topics like:

1. Predictive Analysis
2. Python libraries
3. Scikit-learn, Theano
4. Pylearn2
5. Sentimental analysis

The book has made it evident that Machine Learning is going to be the future and using Python is one of the best ways to execute the Machine Learning tasks.

It will be useful for individuals who are keener in terms of asking questions of their data. The individuals who are looking to improve or extend their capabilities of Machine learning systems.
The only requirement to read through this book is to make sure the individual has good working knowledge in Python.

Book 10: Deep Learning (Adaptive Computation and Machine Learning Series)

Written by Ian  Goodfellow, Yoshua Bengio and Aaron Courville

This book was released in the year 2016 and in no time it has become one of the favourite books in the machine learning world. It is considered or marked as one of the best sources for individuals who are looking to study on Deep learning concepts.

The book has been contributed by three industry is known writers and it caters towards university-level students and also helpful for individuals who are working an entry-level position in the machine learning domain.

Main topics that are included in the book are:

1. Convolution technical topics
2. Generative Models and its hidden layers
3. Algorithms etc

This book will be ideal for individuals who are looking to build their expertise in Deep learning. This is a great source for individuals to start even before coding.

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Conclusion:

All these books are recommendations based on the industry experts and one has to make sure that they have a habit of reading as many books as possible because it does help in terms of gaining good understanding about the overall concepts.

If you have any suggestions, please do write about it in our Comments section below.